Filtering can be performed by decomposition (e.g., projection) of signals along a new set of bases. Such filtering can be used for image enhancement, such as to process echo information to reject artifacts such as clutter or to enhance or separate information of interest from other data. In this manner, the original echo information can be re-expressed along a new coordinate system such that the unwanted information (e.g., clutter) and the signal of interest are separated using the different bases. Bases describing a non-desirable source signal can be suppressed or rejected, and bases describing a desirable source signal can be enhanced or retained.
Such filtering techniques can be classified using information about the how the new bases are determined, such as including a priori determination of bases or adaptive determination of bases. One a priori approach is the Discrete Fourier Transform (DFT) where the bases are defined as complex exponentials without regard to the underlying data being filtered. Such a DFT approach can be used for zonal frequency-based filtering, such as established by a finite impulse response (FIR) filter topology, or an infinite impulse response (IIR) filter topology. Such zonal frequency-based filtering can be used for clutter rejection in applications such as blood vessel wall filtering for blood flow imaging.
However, such DFT-based methods suffer when the frequency characteristics of the non-desirable and desirable signal components overlap. Moreover, in medical ultrasound imaging applications, the non-desirable and desirable signal characteristics can often shift dramatically both space and time due motion, changes in physiology, or spatial variation in tissue structure.